Neck and shoulder landmark detection is a growing development in object detection and segmentation. Such technology can be applied in many applications including augmented reality and intelligent human-computer interaction in PCs, mobile devices, web applications, and the like. Some neck and shoulder landmark detection techniques are based on general image segmentation based on skin color or background and foreground subtraction; or learning-based methods like snake and graph cut algorithms, and the like. However, in many images, the neck is totally covered or partially covered by cloth. This renders the techniques based on general image segmentation rather unsuccessful. Moreover, visually cluttered environments may also cause bad image segmentation and bad edge detection results. In many cases, the detected shoulder line is not smooth or blended with other regions. Thus, it cannot be applied for high-level applications such as virtual cloth-fitting. In addition, the computational complexity in the existing techniques is high and limits their usage in real-time applications.